We help the biggest and brightest to have confidence
in their data, in-silico models and decisions.
PREMIER Virtual Environmental Risk Laboratory
We are part of a multi-disciplinary team delivering a new EU-funded platform for the prioritization and risk evaluation of Active Pharmaceutical Ingredients in the environment. We are developing the digital assessment system at the heart of the PREMIER IMI2 project, working in partnership with AstraZeneca, GlaxoSmithKline, Bayer, Bristol-Myers Squibb, Novartis, Sanofi, Eli Lilly, Merck and many others.
Supporting EPV Transparency at AstraZeneca
AstraZeneca use our EPV Dashboard to demonstrate EcoPharmacoVigilance transparency on astrazeneca.com. AstraZeneca undertake a quarterly search of scientific literature for reports of detection of environmental residues of their active pharmaceutical ingredients (APIs) in order to understand real world environmental risks. Our EPV Dashboard presents these measured concentrations in a risk relationship relative to the Predicted No Effect Concentrations (PNECs) used for environmental assessment.
Reason for Goal Structuring Notation Safety Cases
A world-leading personal care company has been using our Reason collaborative argument diagramming software since 2016 to assist with their toxicology risk assessments.
3Rs Focused Virtual Disease Laboratories
Funded by the NC3Rs (focused on the 3Rs for the replacement, refinement & reduction of animal testing for scientific purposes), we have developed virtual disease laboratories to reduce/replace the need for pre-clinical animal models in therapeutic discovery/development for immune mediated inflammatory diseases like Sarcoidosis and Leishmaniasis and to de-risk clinical trial design for toxicology testing.
Maximise for Data Sharing and Automating Toxicity Predictions
Funded by the NC3Rs, we partnered with Syngenta, Corteva and the University of York to develop the Maximise software platform, combining modelling with data sharing and reuse and automating toxicity prediction for new formulations. Maximise integrates data from multiple sources and employs machine learning to classify data & protect sensitive data that is shared.
Optimisation of Mathematical Models
We have worked with one of the world’s largest biotech companies to re-implement a complex mathematical model for the trafficking of CAR T-cells, converting C and Python code into high-performance software. Our Simomics Evidence technology makes the code more accessible and reusable, exposing the provenance of data, evidence and assumptions.